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British Journal of Healthcare and Medical Research - Vol. 9, No. 5
Publication Date: October, 25, 2022
DOI:10.14738/jbemi.95.13309. Mahwish, N., Saherawala, B. A., & Jhancy, M. (2022). Clinical Decision Making in Dysmorphology- Emerging Role of Artificial
Intelligence. British Journal of Healthcare and Medical Research, 9(5). 366-374.
Services for Science and Education – United Kingdom
Clinical Decision Making in Dysmorphology- Emerging Role of
Artificial Intelligence
Nayesha Mahwish
Department of Pediatrics, Ras Al Khaimah College of Medical
Sciences (RAKCOMS), RAK Medical and Health Sciences
University (RAKMHSU), Ras Al Khaimah, United Arab Emirates
Batul Abdeali Saherawala
Department of Pediatrics, Ras Al Khaimah College of Medical
Sciences (RAKCOMS), RAK Medical and Health Sciences
University (RAKMHSU), Ras Al Khaimah, United Arab Emirates
Malay Jhancy
Department of Pediatrics, Ras Al Khaimah College of Medical
Sciences (RAKCOMS), RAK Medical and Health Sciences
University (RAKMHSU), Ras Al Khaimah, United Arab Emirates
ABSTRACT
The human genome codes for more than 22,000 genes, many of which have been
implicated in human diseases. These genetic diseases are often associated with
dysmorphic facial features. Dysmorphic features occur due to premature closure of
cranial sutures resulting in changes in skull shape and facial characteristics.
Assessment of dysmorphic features is a crucial component of genetic consultations.
This requires a great deal of clinical experience and expertise and tends to be
subjective. Artificial intelligence-based analysis can come in handy for quick and
accurate identification of dysmorphic features. This review explores the role played
by artificial intelligence in identifying dysmorphic facies and diagnosing various
genetic diseases in children.
Keywords: artificial intelligence, dysmorphism, facial recognition technology, genetic
disorders
INTRODUCTION
Genetic disorders affect a large proportion of the human population. Individuals affected by
these diseases suffer from multiple comorbidities such as congenital heart diseases, respiratory
problems, and developmental delays. Early diagnosis can help prevent these comorbidities
thus, improving the quality of life of these patients [1].
Dysmorphic facial features occur in over 1500 different human genetic syndromes. These
dysmorphic features are quite distinct to each disorder [2]. Downs syndrome, for example, is
distinguished by a flattened facial profile, upward slanting palpebral fissures, small ears, a
protruding tongue, and extremity variations [3]. Another genetic disorder, Noonan syndrome,
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Mahwish, N., Saherawala, B. A., & Jhancy, M. (2022). Clinical Decision Making in Dysmorphology- Emerging Role of Artificial Intelligence. British
Journal of Healthcare and Medical Research, 9(5). 366-374.
URL: http://dx.doi.org/10.14738/jbemi.95.13309
displays characteristics such as hypertelorism, down slanted palpebral fissures, ptosis, a
depressed and wide nasal bridge, and low set ears [4].
Identifying dysmorphic facial features aids in the early identification and diagnosis of genetic
disorders. It forms an essential component of genetic consultations. However, it requires a great
deal of clinical experience and expertise. There may be subjective variations in assessment of
dysmorphic facies by different clinicians [5]. Hence, recognizing dysmorphic features is a
daunting task.
With recent advances in the field of artificial intelligence, facial recognition systems have been
developed that assist in the screening and diagnosis of genetic disorders [6]. Deep learning
systems can identify and distinguish genetic conditions based solely on facial phenotyping.
They have been shown to be more accurate than human experts in phenotype recognition of
genetic diseases [7].
In this review, we summarize the role played by artificial intelligence (AI) in identification of
dysmorphic facial features in children with various genetic disorders.
METHODOLOGY & DATA ANALYSIS
Journal articles published from January 2010 to October 2021 about application of artificial
intelligence in identification of dysmorphism were collected from the databases ProQuest,
PubMed, Scopus and Medline using the key words Artificial intelligence OR AI AND
identification OR diagnosis AND dysmorphology OR dysmorphism AND children AND genetic
disorders. The articles were reviewed. The method is represented below in figure I.
Fig.I Methodology
RESULTS
Facial recognition technology
Artificial intelligence (AI) allows computers to solve complex problems and turn raw data into
meaningful data to be used to classify syndrome types. It uses convolutional neural networks
(CNNs) that have massive neurons, layers, and connectivity [8]. Systems such as DeepFace use
Keyword search (Artificial intelligence OR AI AND
identification OR diagnosis AND dysmorphology OR
dysmorphism AND children AND genetic disorders)
37 articles
4 articles
duplicated 33 articles
10 articles
excluded 23 articles
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British Journal of Healthcare and Medical Research (BJHMR) Vol 9, Issue 5, October - 2022
Services for Science and Education – United Kingdom
CNNs while being trained on large amounts of training data to reach performance at the level
of humans.
The most widely used facial recognition application by geneticists is Face2Gene [created by
Facial Dysmorphology Novel Analysis (FDNA Inc., Boston, Massachusetts, USA)] [9]. The
Face2Gene application is readily available to download onto a mobile device and can be used
worldwide wherever there is access to the internet. Face2Gene has a large and an ever-growing
of database of digital images of syndromic patients acting as a reference for the system. Real
life images are taken and processed by a landmark detection algorithm to geometrically
standardize the patient’s face for face verification and reduce pose variation. Ratios between
the points are calculated and compared to a holistic patient ratio. The system is then trained on
these images from a large data set with the help of clinicians identifying patients. The system
then can give a diagnosis on what a patient’s gestalt is best matched to a syndrome [10].
Genetic disorders identified by facial recognition technology
Artificial intelligence-based systems have been tried and tested for the identification of various
genetic disorders ranging from common disorders such as Turners syndrome to rare disorders
such as Phosphomannomutase-2 deficiency (Table I).The clinical trials have been discussed
below.
Noonan syndrome
Noonan syndrome is an autosomal dominant/recessive disorder. Some of its features include
short stature, craniofacial dysmorphism, cardiac abnormalities, short and/or webbed neck, and
cryptorchidism in male patients. This syndrome occurs due to mutations in genes encoding
proteins of the RAS-MAPK signaling pathway, resulting in pathway dysregulation. 15 such
genes have been identified so far: PTPN11, SOS1, RAF1, BRAF, HRAS, KRAS, NRAS, SHOC2,
MAP2K1, MAP2K2, CBL, RIT1, RASA2, A2ML1, and LZTR1.
In one study, facial images of 60 molecularly confirmed Chinese NS were evaluated with the
Face2Gene Research Application (FDNA Inc., Boston, Massachusetts). The images comprised
six pathogenic variants (PTPN11, SOS1, SHOC2, KRAS, RAF1, and RIT1) of the disorder. Results
showed that the mean accuracy that was achieved by Face2Gene on the original sample set was
28%. Moreover, each gene was accompanied with different facial features, all of which were
distinguished by the application. Patients with SHOC2 pathogenic variants were characterized
by significant macrocephaly and thin sparse hair, while patients with RAF1 pathogenic variants
had prominent foreheads [11].
Angelman syndrome
Angelman syndrome is a neurogenetic disorder characterized by developmental delay,
excessive laughter, absent or severely limited speech, seizures with a characteristic
electroencephalogram (EEG) and microcephaly. Characteristic facial features include wide
mouth, protruding tongue, midface recession and prognathism. AS occurs due to deficient
expression of gene UBE3A on chromosome 15. Different molecular subtypes of AS are
characterized by minor differences in facial features. Identification of these subtle facial
features could guide genetic testing for AS patients and their families.